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CONSUMER BEHAVIOR AND PURCHASE DECISION
TOWARDS LINEMAN (AN ON DEMAND FOOD
DELIVERY) IN BANGKOK
BY
MISS NOPPANUT TALABPETCH
AN INDEPENDENT STUDY SUBMITTED IN PARTIAL
FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE PROGRAM IN MARKETING
(INTERNATIONAL PROGRAM)
FACULTY OF COMMERCE AND ACCOUNTANCY
THAMMASAT UNIVERSITY
ACADEMIC YEAR 2017
COPYRIGHT OF THAMMASAT UNIVERSITY
Ref. code: 25605902040756PCR
CONSUMER BEHAVIOR AND PURCHASE DECISION
TOWARDS LINEMAN (AN ON DEMAND FOOD
DELIVERY) IN BANGKOK
BY
MISS NOPPANUT TALABPETCH
AN INDEPENDENT STUDY SUBMITTED IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE
OF MASTER OF SCIENCE PROGRAM IN MARKETING
(INTERNATIONAL PROGRAM)
FACULTY OF COMMERCE AND ACCOUNTANCY
THAMMASAT UNIVERSITY
ACADEMIC YEAR 2017
COPYRIGHT OF THAMMASAT UNIVERSITY
Ref. code: 25605902040756PCR
(1)
Independent Study Title CONSUMER BEHAVIOR AND PURCHASE
DECISION TOWARDS LINEMAN (AN ON
DEMAND FOOD DELIVERY) IN BANGKOK
Author Miss Noppanut Talabpetch
Degree Master of Science Program in Marketing
(International Program)
Major Field/Faculty/University Faculty of Commerce and Accountancy
Thammasat University
Independent Study Advisor Associate Professor James E. Nelson, Ph.D.
Academic Year 2017
ABSTRACT
Due to the upward trend of mobile internet users and the increasing
demand in connecting an online platform to offline businesses, O2O e-commerce has
been developed and becomes the next trend that helps connect offline service
providers to customers. LINE MAN is a pioneer of O2O platform in Thailand which
provides an on-demand delivery service. Growth opportunity in Thai market is
considerably high if entrepreneurs can develop a technology to enhance their business
and have a good understanding on consumers’ behavior and purchase decision
towards online to offline platform.
This study investigated a contemporary topic in applied marketing
focusing on technology area of marketing knowledge. Objectives of this research are
to understand consumer behavior in adoption process and barrier to adoption factors
towards LINE MAN, an on-demand food delivery service, as well as identify
influencing of motivation factors on adoption of on-demand food delivery service,
and examine consumer segmentation.
Results showed the most working women in Thailand are likely to adopt
on-demand food delivery service of LINE MAN. They are trendy and trust in online
shopping environment. Their beliefs in O2O value are the pursuit of quick and easy,
high quality and inexpensive. Personal influences is the main impact to their attitude,
trial, and purchase decision. On the other hand, the main barrier to adopt this service
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is in a stage of need recognition. Most non-users did not consider or have no problem
about finding their meals. Surprisingly, technology capability is not an obstacle.
Keywords: LINE MAN, On-demand Food Delivery Service, O2O e-commerce
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ACKNOWLEDGEMENTS
I would like to express my very great appreciation to Associate Professor
James E. Nelson, Ph.D. for his valuable, expertise and constructive suggestions during
the planning and development of this study. His willingness to give his time so
generously has been very much appreciated. My grateful thanks are also extended to
my friends and every participants who have made this study a success through their
support. Finally, I wish to thank to MIM community and Thammasart University for
such a great knowledge and experience through my study.
Miss Noppanut Talabpetch
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TABLE OF CONTENTS
Page
ABSTRACT (1)
ACKNOWLEDGEMENTS (3)
LIST OF TABLES (7)
LIST OF FIGURES (8)
LIST OF ABBREVIATIONS (9)
CHAPTER 1 INTRODUCTION 1
1.1 Statement of Problems 1
1.2 Research Objective 2
CHAPTER 2 REVIEW OF LITERATURE 3
2.1 Consumer Trend in the Mobile Internet Era in Thailand 3
2.2 Current Situation of LINE and LINE MAN in Thailand 3
2.3 Consumption Psychology and Consumption Behavior in the Mobile
. Internet Era 4
2.4 Online to Offline e-commerce (O2O) 5
2.5 Consumer Behavior towards O2O Platform 6
2.6 Consumer Adoption for New Products or Technology 7
CHAPTER 3 RESEARCH METHODOLOGY 9
3.1 Exploratory Research 9
3.1.1 Secondary Research 9
3.1.2 In-depth Interviews 9
3.2 Descriptive Research 10
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3.2.1 Questionnaire Design 10
3.3 Identification of Key Research Variables 10
3.4 Target Population 11
3.5 Data Collection Method 11
3.6 Data Analysis Method 12
CHAPTER 4 RESULTS AND DISCUSSION 14
4.1 Review of In-depth Interview 14
4.2 Characteristics of LINE MAN Users 15
4.2.1 LINE MAN Users 18
4.2.2 Non-Users Awareness 22
4.2.3 Beliefs in Online Behaviors and Lifestyles 22
4.3 Factor Analysis 24
4.4 The Impact of Factors on Implementation of On-demand Food
. Delivery Service 26
4.5 Clusters Analysis 28
4.5.1 Defining Clusters 289
4.5.2 Consumer Segmentations 30
CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS 31
5.1 Conclusion 31
5.2 Recommendation 33
5.3 Limitation of This Study and Future Research 33
REFERENCES 34
APPENDICES 37
APPENDIX A: Questionnaire Design 38
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APPENDIX B: SPSS Data Result 45
BIOGRAPHY 49
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LIST OF TABLES
Tables Page
3.1 Sampling Plan 12
4.1 Characteristic of in-depth interview’s respondents 14
4.2 Respondent’s demographic characteristics 16
4.3 Respondent’s awareness of LINE MAN 17
4.4 Familiarity with LINE MAN services 17
4.5 The classification of LINE MAN users 18
4.6 Reasons to trial LINE MAN services 18
4.7 Respondent’s degree of information searching on-demand food
delivery service 19
4.8 Respondent’s occasion of applying on-demand food delivery service 19
4.9 Respondent’s reasons of applying on-demand food delivery service 20
4.10 Purchasing factors affecting on choosing an on-demand food
delivery service 21
4.11 Barriers of using an on-demand food delivery service 22
4.12 Respondent’s beliefs toward online behavior on degree of
the pursuit on each characteristic 23
4.13 Respondent’s agreement toward lifestyle 24
4.14 The most used mobile applications (Top 3) 24
4.15 Pattern Matrix of Factor Loadings (Promax Rotation) 25
4.16 New Labels of factor analysis 25
4.17 The impact of purchasing factors and reasons to use on
implementation of on-demand food delivery service 27
4.18 The impact of extracted purchasing factors on implementation of
on-demand food delivery service 28
4.19 Final clustering and ANOVA table 29
4.20 The segmentation of consumers based on mobile applications 30
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LIST OF FIGURES
Figures Page
2.1 AIDMA and SCIAS models 5
2.2 Operation flow of O2O e-commerce 6
3.1 Independent variables and Dependent variables 11
5.1 The result of respondents towards adoption model and
decision making process 31
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LIST OF ABBREVIATIONS
Symbols/Abbreviations Terms
O2O Commerce Online to Offline Commerce
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CHAPTER 1
INTRODUCTION
1.1 Statement of Problems
With the development of smart phone and telecommunication technology,
this enables people to access the internet easily and more convenient from anywhere
and at any time they want. Along with this, the rising trend of e-commerce has seen
through various innovations. Currently, a new e-commerce model is O2O which
refers to Online to Offline. This concept was first introduced by Alex Rampell in
2010. He defined as “the business model that leverages online channels to acquire
offline services and products” (Rampell, 2017). The integration of these
advancements transform people’s daily’s live in term of consumption psychology and
consumption behavior.
Since 2014, LINE was introduced in Thailand as a messaging application
and became the most popular chatting platform for Thai users. Online to Offline
model is new to Thailand. LINE MAN is one of LINE’s businesses that introduced an
initial O2O platform to the market in 2016 and it was very successful. Thus, with the
explosive growth of mobile internet user in Thailand, O2O will not only create new
opportunities for all industries, but also create benefits for consumers. However,
understanding Thai consumers’ behavior and purchase decision criteria towards this
technology is a key to have successful in O2O business. Therefore, LINE MAN is a
good case study for online to offline business model and for better understanding of
consumers’ behavior towards on demand food delivery service in the mobile internet
era. Also, the findings from this research will advantage the restaurant industry to
create marketing strategy and expand distribution channels through the development
of O2O commerce.
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1.2 Research Objective
As indicated in the introduction, online to offline model of LINE MAN
creates the opportunity for the restaurant businesses who are introducing or expanding
their products or services to consumers via the world of mobile internet technology. In
the other words, this attributes to customers easier access to products and services as
well as increasing traffic and brand visibility to the businesses. Besides, this will be
beneficial for those who aim to develop their business by using online channel to
acquire offline services and products in the growing mobile commerce in Thailand. If
this model is used efficiency, it will create a huge profit for enterprise. However, to
achieve those things, business owners or marketers should understand consumer
behavior and purchase decision towards LINE MAN food delivery service. Therefore,
there are three main objectives of this research.
Objectives for the study:
1. To understand consumer behavior in adoption process and barrier to
adoption factors towards LINE MAN, an on-demand food delivery
service.
2. To identify motivation factors such as save time, save cost, and save
energy and key purchasing factors such as system quality, product and
delivery quality, trust, and price towards LINE MAN, an on-demand
food delivery service.
3. To determine characteristic consumers’ behavior of LINE MAN, an
on-demand food delivery service and identify consumer segmentation
by 1) demographics such as age, gender, income, etc. and 2)
psychographics such as activities, preferences, and interests towards
online channel.
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CHAPTER 2
REVIEW OF LITERATURE
2.1 Consumer Trend in the Mobile Internet Era in Thailand
According to a report from We Are Social, in Thailand 2017, there are 46
million people (67%) who have accessed the internet and this is a significant growth
of 21 percent which compared with the 2016 year (Kemp, 2016). National statistical
indicated most of the users are connected to the internet through their smartphones
about 90 percent in 2016. Due to the exponential mobile internet user growth and the
transformation of consumer behavior after launching 3G/4G wireless broadband
Internet in Thailand (Thongtep, 2016), m-commerce (mobile commerce) becomes a
major channel for shopping and changing consumer shopping habits (Meola, 2016).
2.2 Current Situation of LINE and LINE MAN in Thailand
Interestingly, LINE is the most popular messaging application in Thailand
which there was around 41 million active users at the end of 2016. The benefits of
LINE have been proved by the large user bases. Thais spend one-third of the 234
minutes per day on their mobile on LINE or about 70 minutes per day on average
(The Nation, Line aims to morph into ‘mobile platform’, 2017) cited in Nielsen’s
research. To maintain LINE users and to build its own ecosystem, LINE aims to
extend their business models to deliver end-to-end services in everyday life on the
mobile platform. Apart from exchanging messages, LINE will expand its business in
four dimensions including communication, digital content, services, and commerce. It
focuses on making users’ lives easier and more convenient through its platform. For
services and commerce perspective, now it has covered e-commerce, payment
gateway, and transportation (Leesa-Nguansuk, 2017).
In 2016, LINE launched LINE MAN as Thailand’s first O2O platform, an
on-demand delivery service. It has started to offer services 24 hours daily with four
categories which are food delivery, convenience goods, messenger, and postal.
Currently, LINE MAN has 500,000 users and the number has been increased over
10% per month (Tortermvasana, 2017). With the increasing demand of O2O
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commerce, it has been promoted the growth of LINE MAN users. LINE MAN was
very successful as Thailand’s number one food delivery service in 2017 (The Nation,
Line man celebrates first anniversary, aims to be No. 1 in Thailand, 2017)
2.3 Consumption Psychology and Consumption Behavior in the Mobile Internet
Era
The development of mobile and information technology makes consumers
get information more convenient and fast. It leads to quickly transformation of
consumption psychology and behaviors. The psychological characteristic and the
activities process of the consumption in the mobile internet era base on four common
characteristics which are personality, speed, trust, and sharing. However, consumption
behavior have been changed from the classical theory of information flow in AIDMA
model (Attention-Interest-Desire-Memory-Action) to SCIAS model (Search-
Compare-Interest-Action-Show). To search is the first step when consumers want to
buy products or use services in the mobile internet era because it can be through the
search engine online quickly and easily. Consumers will receive a lot of information
and reference from other consumer evaluations. Then, they compare details with the
similar goods or services, also combine with their needs. If they are interested in a
certain selection, they make action in purchase decision. To show comes out after the
results of consumption. Importantly, SCIAS model is without the end point of
information processing. The next transaction after the completion of the first
transaction will be due to the results in show stage. In addition, it is possible to
directly contribute to other consumers’ decision making. (Cao, 2015)
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Figure 2.1: AIDMA and SCIAS models
2.4 Online to Offline e-commerce (O2O)
Recently, a new trend of e-commerce model is online to offline (O2O) or
on-demand service that attracts online consumers to experience offline services. With
the advancement of digital technology, it enables business to build multi-platform tool
for marketing and e-commerce use by using O2O model to pull the customer closer.
The objective of O2O commerce focuses on profit maximization of the consumption
value for the consumers by using online platform (Ye, 2015). On the contrary, Omni-
channel marketing is multiple channels work seamlessly together to create total
customer experience (Kotler & Keller, 2016)
The key idea of O2O model is to enhance consumer awareness on online
channels and drive consumer to visit or using offline service. Consumers can research
and request the service on the online platform, but physically experience the reserved
service occur at an offline site. The operational flow of O2O e-commerce include
three main parties which are 1) O2O platform, 2) Consumer, and 3) Offline
businesses. Among three parties, there is an exchange information throughout the
operational flow without the end point. Basically, O2O platform connects between
offline businesses and consumer via online platform. Through this platform, offline
businesses can send information and promote their products and services while
consumer can search and collect the information. Then, consumer makes decision and
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purchases through the online platform. The personal demand will be sent to the offline
businesses through this platform. Also, payment can be paid immediately. Finally,
consumers’ feedback after experiencing the service or consuming the product can be
submit and send directly to the offline businesses (Ye, 2015)
Figure 2.2: Operation flow of O2O e-commerce (Ye, 2015)
2.5 Consumer Behavior towards O2O Platform
LINE MAN is one of a brand extension strategy of LINE by using O2O
model to maintain and expand its revenue streams. Based on theory of reasoned action
(TRA) for understanding people’s adoption intention, it asserts that user’s intentions
in using the new service is influenced by positive attitudes and trust towards the
proposed brand extension. (Hwang & Kim, 2017). However, consumer buying
behavior is the outcome of the consumers that they purchase to satisfy their needs and
wants which depending on the personal factors such as age, psychology and
personality. From model of customer satisfaction, there are three main factors which
are 1) e-commerce system quality, 2) product and delivery quality, and 3) perceived
price (Lien, Chang, & Lin, 2017). For personal factors, it defines as characteristics of
O2O consumers’ behavior which are 1) the pursuit of quick and easy, 2) the pursuit of
high quality and inexpensive 3) the pursuit of personality, 4) the pursuit of
consumption experience and 5) decline in customer loyalty (Chiu & Yen, 2016).
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Interestingly, the study of O2O platforms in China found that sales promotion is the
most important stimuli for purchase decision on O2O food delivery (Ye, 2015)
2.6 Consumer Adoption for New Products or Technology
O2O platform is new to Thai consumers. For new products or new ideas,
marketers generally target to early adopters and use the theory of innovation diffusion
and consumer adoption to identify them. According to Everett Rogers, the consumer-
adoption process is the mental steps of an individual consumer that starts from the
first hearing to final adoption. The stages of Innovation-Adoption Model consist of
awareness, interest, evaluation, trial, and adoption respectively. However, the level of
innovativeness of an individual consumer is different. Everett Rogers defines as “the
degree to which an individual is relatively earlier in adopting new ideas than the other
members of his social system. It is divided into five groups which are Innovators,
Early adopters, Early majority, Late majority, and Laggards (Kolter & Keller, 2016).
When consumers consider to purchase, the pre-purchase stage begins with personal
motivations (stimulus) which may cause from a commercial cue, a social cue, or a
physical cue. Then, it is followed by problem awareness, information search, and
evaluation of alternative (Hoffman, 2011)
Based on the previous studies, there are a few researches done to
understand adoption and barriers toward on-demand food delivery service (O2O
platform) and focusing on consumer behavior, decision making process, and
purchasing factors in the context of Thailand. As Thai characteristic and culture
values might differ from other countries, this is a gap that the author’s interest to
study about this relationship for the country by using LINE MAN as a case study.
Because of fast growing m-commerce and O2O model, adoption issues in on-demand
food delivery service have been significantly related to consumers’ behavior. It is
influenced by positive attitudes and beliefs in an online platform which are 1) the
pursuit of quick and easy, 2) the pursuit of high quality and inexpensive 3) the pursuit
of personality, 4) the pursuit of consumption experience and 5) decline in customer
loyalty. Additionally, to satisfy consumer needs and want, it depends on personal
factors (psychographic and demographics) and satisfaction or purchasing factors
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including system quality, product and delivery quality, and perceived price. These
have been the main topic done by other researches. However, there are still not many
studies being done to study on other factors or relationship that might contribute to
adoption in on-demand food delivery service. Consequenlty, this research was
adapted a few variables and model from previous studies and designed the framework
as a reference for this study to understand consumer adoption for new services or
technology in Thailand.
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CHAPTER 3
RESEARCH METHODOLOGY
The research were conducted by exploratory research and descriptive
research. The exploratory research was included secondary research and in-depth
interviews of 10 respondents which provided insights about consumer behavior and
attitude towards LINE MAN. The findings from the exploratory research were then
validated through descriptive research. The descriptive research was conducted by
online survey questionnaire with target of 200 respondents. Sampling selection was
from current target consumers who were Bangkok millennial and knew LINE MAN
application. Due to time constraint, the sampling method of this research was
convenience sampling method. After data collection completes, the data was analyzed
by using SPSS to achieve the research objectives.
3.1 Exploratory Research
3.1.1 Secondary Research
The purpose of secondary research is to understand mobile internet users’
overview in Thailand, LINE and LINE MAN current situation in Thailand, online to
offline commerce overview, overall consumer behavior towards online to offline
approach (Objective 1) , and primary consumer purchasing factors of O2O commerce
(Objective 2). The secondary data was obtained from many credible sources including
websites, market research publication, academic journals, and books.
3.1.2 In-depth Interviews
In-depth interviews were conducted to explore consumer insights in terms
of adoption process and consumer behavior towards on-demand food delivery service
of LINE MAN comparing to their original buying behavior ( Objective 1) .
Interviewees were asked to list their motivation and purchasing factors for using on-
demand food delivery service of LINE MAN (Objective 2) . Consumers’ profile and
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their lifestyle was also collected ( Objective 3) . The in-depth interview was piloted
with four respondents, and followed by six respondents. The participants were
included non-users who knew LINE MAN but never use and all LINE MAN users
regardless of their frequency in using the service.
3.2 Descriptive Research
The descriptive research was conducted by online survey questionnaire.
The questionnaire was designed based upon the information from exploratory
research, aimed for 200 respondents. The questionnaire took approximately 10 - 15
minutes. The responses were interpreted and analyzed to achieve all the research
objectives (Objective 1, 2, 3).
3.2.1 Questionnaire Design
The some part of questions survey was a guideline from the result of in-
depth interview which was designed to achieve research objective. In addition, the
questionnaire consists of five parts; (See the appendix A)
Section 1: Screening Question for selecting only target population.
Section 2: Adoption behaviors and process towards on-demand food
delivery service.
Section 3: Purchasing factors towards on-demand food delivery service
Section 4: Characteristics of consumer behavior and motivation factors
towards on demand food delivery service.
Section 5: Consumer’s demographics and psychographics.
3.3 Identification of Key Research Variables
Key variables of this research were 1) Consumer demographics 2)
Consumer psychographics such as activities, preferences, and interests towards online
platforms 3) Consumer behaviors such as a usage rate of mobile applications and
online food delivery 4) Purchasing decision such as motivation factors (save time,
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more convenience and less stress) and barriers to adoption for a new product
(awareness, interest, trial, and adoption). The independent variables and dependent
variables of this study were described in Figure 3.1
Figure 3.1: Independent variables and Dependent variables
3.4 Target Population
For both in-depth interview and questionnaire survey the target
respondents were qualified as follow: were Bangkok millennial, age 20 - 36 years old,
male and female, who knew LINE MAN application whether they used to try the
service or not.
3.5 Data Collection Method
The non-probability (convenience) samples were used for both in-depth
interview and survey questionnaire. The data collection process were conducted in
Bangkok, approximately three weeks during 28 November - 21 December 2017 for
in-depth interviews and three weeks during 14 - 31 January 2018 for survey
questionnaire. Research methodologies and the sample size for each method was
illustrated in
Table 3.1: Sampling Plan
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Table .
At first, the pilot test were conducted for all research methodologies
including 4 respondents of in-depth interview, and 5 respondents of online survey
questionnaire. All of respondents were qualified, as indicated in the content 3.4)
Target Population, through the screening questions.
For in-depth interview, personal contact were used as a method to recruit
qualified respondents from various consumer’ s profile. In-depth interviews were
conducted with 6 respondents by face-to-face interview or telephone interview
depends on respondents’ preference.
For survey questionnaire, the online questionnaires were distributed
through e-mail, online food review community, and social network platform such as
Facebook and Line. Responses from online surveys were transmitted to the researcher
immediately via the online system.
Table 3.1: Sampling Plan
Type of research Methodology Pre-test pilot Sample size
1. Qualitative In-depth Interview 4 respondents 6 respondents
2. Quantitative Online Survey
Questionnaire
3 respondents 195 respondents
3.6 Data Analysis Method
The key findings from exploratory research were used as a guideline to
create the descriptive research. The results from questionnaire survey were grouped
and coded as necessary to ensure data accuracy and prepare data for analysis. Then, in
Chapter 4, data analysis provided data analyses and interpretation that can clearly
response to the research questions and objectives. There are three objectives represent
in this research required to accomplish, which aim to understand consumer behavior
in adoption process and barrier to adoption factors towards LINE MAN, an on-
demand food delivery service, as well as identify influencing of motivation factors on
adoption of on-demand food delivery service, and examine segmentation of
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consumers base on demographics, psychographics, consumer behavior, and lifestyle.
Statistical Package for the Social Sciences (IBM SPSS) is selected for analyzing the
relationship between variables. Statistical methods include both descriptive and
inferential statistics. Descriptive analysis was applied to summarize respondent’s data
and their opinion toward questions. Both hierarchical and k-mean cluster analysis was
applied for clustering group of consumers. Inferential analysis includes one-way
ANOVA, independent t-test was applied to examine the difference among users and
clustered groups. Lastly, multiple linear regression was applied to examine the effect
of factors on adoption of LINE MAN.
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CHAPTER 4
RESULTS AND DISCUSSION
4.1 Review of In-depth Interview
The in-depth interviews were conducted on December 20 and 26, 2017
with 6 participants. All participants were Bangkok millennial and have experienced
about on-demand food delivery service. These included non-users who knew LINE
MAN but never use and LINE MAN’s users, regardless of their frequency in using the
service. The main purpose was to understand key variables (Adoption process,
barriers, and purchasing factors) which were applied on the area of research.
Moreover, the interview had the objective to explore other influence aspects related to
the study topic.
Table 4.1: Characteristic of in-depth interview’s respondents
Characteristic Type Gender Age Occupation
Respondent 1 Non Users Female 26 Accountant
Respondent 2 Users Female 32 HR Manager
Respondent 3 Users Female 34 Senior Officer
Respondent 4 Non Users Male 28 Jewelry Designer
Respondent 5 Users Male 29 Engineer
Respondent 6 Users Male 32 Senior Banker
Based on the interview and model of adoption process from the first
hearing to final adoption, an advertisement of LINE MAN through LINE notification
is the most channel that most of the respondents has awareness towards LINE MAN.
Besides, promotion is the most factor that made all respondents trial the service at the
first time. However, all interviewees said there is no relation between LINE and LINE
MAN in term of quality and no motivation to try or use LINE MAN. The most
motivation factors for using on-demand food delivery service are convenience, save
time, save energy, and save cost respectively. They mentioned good things about
LINE MAN that 1) Linkage to their LINE account, 2) Familiar with LINE’s
experience 3) Clearly price structure which separates delivery price and calculated
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from distance 4) Track and trace status 5) Customized order. These were applied for
purchasing factors of on-demand food delivery services.
For users, price is a key to separate their purchasing decision. The users
who used average more than 2 times per month, price is less important criteria.
Service and delivery quality in term of the coverage and diversity of restaurants,
delivery time, and tracking status are the important factors that they concerned. They
prefer online payment. On the other hand, the users who used the service average less
than 2 time per month, the price is the most factors of purchasing. They search and
compare price or promotion before making decision and prefer cash payment.
For non-users, barriers prevent them to try were 1) no interest or problem
about food delivery 2) no consideration on this service 3) complication for
downloading application. If they were busy, they prefer to find something quick and
easy to eat such as convenience stores or local restaurants nearby them. Moreover,
they like to have an experience in the restaurant.
4.2 Characteristics of LINE MAN Users
The author selected frequency and percentage to identify demographic
information of 206 respondents. According to table 4.2, 148 of 206 respondents were
female, which considered them as majority representative of the study (71.8%). There
are four age groups in this research, and found most of them aged 31-36 years old
(51%), followed by 27-30 years old (30.1%), 23-26 years old (17.5%), and 20-22
years old (1.5%). The mean of age is 33 years old. The majority of respondents’
education level is Bachelor’s degree (55.8%) and followed by Master’s degree
(41.7%). Moreover, more than half of respondents were employed in private sector
(64.6%), followed by business owner (10.7%), government (9.7%), others (7.8%) and
freelance (7.3%). The largest income group found in this research is 20,000-40,000
THB (41.7%), followed by 40,001-60,000 THB (14.6%), 40,001-60,000 THB
(13.6%), above 80,001 THB (13.1%), less than 20,000 THB (11.2%), and 60,001-
80,000 THB (5.8%).
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Table 4.2: Respondent’s demographic characteristics
Demographics Item Frequency Percentage (%)
Gender
(n = 206)
Male 58 28.2
Female 148 71.8
Age
(n = 206)
20-22 years old 3 1.5
23-26 years old 36 17.5
27-30 years old 62 30.1
31-36 years old 105 51
Mean (33 years old)
Education
(n = 206)
High school graduate 1 0.5
Diploma or the equivalent 2 1
Bachelor’s degree 115 55.8
Master’s degree 86 41.7
Doctorate degree 2 1
Median (Bachelor’s degree)
Occupation
(n = 206)
Government 20 9.7
Private Sector 133 64.6
Business Owner 22 10.7
Freelance 15 7.3
Others (Studying or Unemployed) 16 7.8
Income
(n = 206)
< 20,000 THB 23 11.2
20,000-40,000 THB 86 41.7
40,001-60,000 THB 28 13.6
40,001-60,000 THB 30 14.6
60,001-80,000 THB 12 5.8
> 80,001 THB 27 13.1
Median (20,000-40,000 THB)
From table 4.3, it shows 183 respondents or 88.8% has known LINE
MAN application. The result also describes that most of them knew it from their
friend’s suggestion (35%), followed by advertisement or notification in LINE
application (30.6%), and advertisement in social media (Facebook) (27.3%).
However, it found that only 92 respondents or 50.3% was adopted LINE MAN for on-
demand food delivery service. Thus, it can be classified consumers in this research
into two groups, as users and non-users.
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Table 4.3 Respondent’s awareness of LINE MAN
Segmentation Usage rate (times/month) Frequency Percentage (%)
Awareness of LINE
MAN application
(n =206)
Yes 183 88.8
No 23 11.2
Knowing of LINE MAN
by first time
(n = 183)
I saw its advertisement or notification
in LINE app.
56 30.6
I saw its advertisement in the
restaurants.
3 1.6
I saw its advertisement in social media
(Facebook)
50 27.3
I saw LINE MAN drivers on the road. 10 5.5
My friends talked about it or
suggested to use.
64 35.0
The implementation of
LINE MAN application
(n = 183)
Yes 92 50.3
No 91 49.7
From table 4.4, food delivery (Mean = 3.69, SD = 0.82) is the most
service that respondents are familiar with LINE MAN, followed by messenger (Mean
= 3.33, SD = 0.79), and sending document (Mean = 3.16, SD = 0.89). Delivery for
grocery (Mean = 2.36, SD = 1.03) is the service that they are less familiar with.
However, most of them knew about the services, they have not ever try.
Table 4.4: Familiarity with LINE MAN services
LINEMAN
Services
Not at all
familiar
Slightly
familiar
(I’ve heard
but did not
know about
it)
Somewhat
familiar
(I knew it
but never
use)
Moderately
familiar
(I knew it
and used to
try)
Extremely
familiar X̄ SD
Food delivery 1 3 84 59 36 3.69 .82
Messenger 5 5 115 41 17 3.33 .79
Groceries 5 5 115 41 17 2.36 1.03
Document
and Postal
10 12 116 28 17 3.16 .89
The author used frequency distribution of usage rate in question 8 to
categorize the users into light, medium, and heavy by using mean = 1.62 and one
standard deviation to separate the group (SD = 1.83) see also appendix B: Table 1.
Table 4.5 shows most of users are medium users, as they use LINE MAN for on-
demand food delivery services at an average of 2 – 6 times per month (26.7%),
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followed by light users (0-1 time) (12.6%), and heavy users (>7) (5.3%). It found the
largest proportion of participants are non-users who have awareness of LINE MAN
(44.2%), while 11.2% or 23 respondents do not know LINE MAN and other on-
demand delivery services.
Table 4.5: The classification of LINE MAN users
Segmentation Usage rate (times/month) Frequency Percentage (%)
Users
(n =92)
Light (0-1) 26 12.6
Medium (2-6) 55 26.7
Heavy (7 or more) 11 5.3
Non Users
(n =114)
Awareness 91 44.2
No Awareness 23 11.2
4.2.1 LINE MAN Users
According to 92 respondents who are LINE MAN users, it shows the
major reason of adoption this application for on-demand food delivery service is
promotion/discount (52.2%), followed by they want to order food from restaurant that
has a long queue (12%) and want to try a new application or service (12%), and want
to order food that suggested by friends (10.9%).
Table 4.6: Reasons to trial LINE MAN services
Reasons (n = 92) Frequency Percentage
(%)
I liked a promotion/discount. 48 52.2
I searched for food delivery services and I found LINE MAN. 6 6.5
I want to order food and a friend suggested 10 10.9
I want to order food from the restaurant that has a long queue. 11 12
I want to try a new application/new service. 11 12
I was familiar with LINE application and it linked to my LINE account. 6 6.5
Table 4.7 reveals that 32.6% of respondents sometimes do information
search on-demand food delivery service, while 20.7% and 19.6% often or always do
it, respectively. Only 14.1% and 13% was answered rarely and never search
information before using the service.
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Table 4.7: Respondent’s degree of information searching on-demand food
delivery service
Information Search Frequency Percentage (%)
Always 18 19.6
Often 19 20.7
Sometimes 30 32.6
Rarely 13 14.1
Never 12 13
Table 4.8 shows the difference among users in term of occasions. It found
light users mostly want to order food from the specific restaurants where they are
difficult to go (Mean = 4.42), while medium and heavy users do not want to eat
outside or lazy, accounted for 4.64 and 4.73, respectively. The result of one-way
ANOVA and post-hoc analysis revealed the difference among users in some aspects
at 0.05 significant level (Sig. < 0.05). It found that medium and heavy users has
higher agreement toward “I do not want to eat outside or lazy” (Sig. = 0.036) and “I
am busy and do not have time to go outside” when compares to light users (Sig. =
0.006).
Table 4.8 Respondent’s occasion of applying on-demand food delivery service
Occasions
(n = 92)
Light
(n = 26)
Medium
(n = 55)
Heavy
Users
F Sig. Post-hoc
X̄ SD X̄ SD X̄ SD
I want to order food from the
specific restaurants where they
are difficult to go
4.42 0.58 4.24 0.84 4.55 0.69 1.062 .350 No
difference
I am do not want to eat outside
or lazy
4.27 0.87 4.64 0.52 4.73 0.47 3.461 .036 M,H > L
I am busy and do not have
time to go outside
4.04 0.82 4.49 0.63 4.73 0.47 5.468 .006 M,H > L
My friends or family want to
order food delivery.
3.92 0.89 4.31 0.69 4.27 0.65 2.418 .095 No
difference
I do not want to wait a long
queue by myself.
3.81 0.94 4.00 1.02 4.27 0.79 0.917 .403 No
difference
Table 4.9 shows there is no difference among users in term of reasons. It
found that light users (Mean = 3.77), medium (Mean = 3.69), and heavy users (Mean
= 3.82) use an on-demand food delivery service because it save time as the main
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reason. The result of one-way ANOVA and post-hoc analysis revealed no difference
among users at 0.05 significant level (Sig. > 0.05).
Table 4.9 Respondent’s reasons of applying an on-demand food delivery service
Reason
(n = 92)
Light
(n = 26)
Medium
(n = 55)
Heavy
(n = 11)
F Sig. Post-hoc
X̄ SD X̄ SD X̄ SD
Save time 3.77 0.99 3.69 0.96 3.82 1.08 0.108 .897 No difference
Save cost 2.23 1.24 2.36 1.19 3.00 1.10 1.669 .194 No difference
Save energy 2.85 1.08 2.60 1.10 2.27 0.79 1.178 .313 No difference
Less stress 2.12 1.31 2.25 1.35 2.00 1.55 0.207 .813 No difference
Convenient 2.92 1.23 2.71 1.23 3.00 1.67 0.389 .679 No difference
The author did one-way ANOVA and found only two factors (Tracking
system and customized services) that are significantly different among the users’
group. Table 4.10 shows the difference among users in term of factors affecting on
their choosing an on-demand food delivery service as followed.
In term of system quality, it found that light users rated on security of
payment and privacy as it is most important on their decision making (Mean = 4.23),
while medium and heavy users prefer tracking system, accounted for mean 4.31 and
4.45, respectively. The result of one-way ANOVA and post-hoc analysis revealed the
difference among users in some aspects at 0.05 significant level (Sig. < 0.05). It found
that medium and heavy users have higher rated on tracking system than light users
(Sig. = 0.011).
For product and service quality, it found that light users rated on coverage
of delivery area as it is most important on their decision making (Mean = 4.23), as
well as medium and heavy users also rated this factor as it is most important for
choosing an on-demand food delivery service, accounted for mean 4.33 and 4.73,
respectively. The result of one-way ANOVA and post-hoc analysis revealed the
difference among users in some aspects at 0.05 significant level (Sig. < 0.05). It found
that medium and heavy users has higher rated on customized services than light users
(Sig. = 0.026).
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In term of price, promotion, and company reputation, the result shows that
light users rated company reputation as the highest importance for their decision
(Mean = 4.15), while medium and heavy users have the highest score on
promotion/discount, accounted for mean 4.15 and 4.27, respectively. However, the
result of one-way ANOVA and post-hoc analysis revealed no difference among users
at 0.05 significant level (Sig. > 0.05).
Table 4.10: Purchasing factors affecting on choosing an on-demand food delivery
service
Purchasing factors
(n = 92)
Light
(n = 26)
Medium
(n = 55)
Heavy
(n = 11)
F Sig. Post-hoc
X̄ SD X̄ SD X̄ SD
1. System quality
1.1 Easy to make order 3.85 0.88 4.18 0.80 4.36 0.67 2.145 .123 No
difference
1.2 Design of the
platform
3.65 1.02 4.00 0.72 4.18 0.87 2.131 .125 No
difference
1.3 Tracking system 3.77 0.86 4.31 0.79 4.45 0.69 4.778 .011 M,H > L
1.4 Security of
payment and privacy
4.23 0.99 4.31 0.77 4.18 1.33 0.126 .882 No
difference
2. Product and service quality
2.1 Variety of
restaurants
4.19 0.94 4.20 0.87 4.55 0.52 0.796 .454 No
difference
2.2 Provide
information and
reviews of menus and
restaurants
3.65 0.89 3.62 1.03 4.00 0.77 0.725 .487 No
difference
2.3 Coverage of
delivery area
4.23 0.65 4.33 0.72 4.73 0.65 2.036 .137 No
difference
2.4 Delivery on time 4.19 0.94 4.22 0.76 4.36 0.67 0.185 .832 No
difference
2.5 Customized
services
3.65 0.94 4.13 0.72 4.27 0.79 3.813 .026 M,H > L
2.6 Quality of drivers
(taking care of food
and manner)
3.85 0.83 4.13 0.75 4.09 0.83 1.164 .317 No
difference
2.7 Call center or
support services
3.81 0.94 3.53 0.98 3.64 1.12 0.718 .491 No
difference
3. Price 3.96 0.92 3.89 0.90 3.91 0.70 0.057 .945 No
difference
4. Promotion/
Discount
4.12 0.99 4.15 0.93 4.27 0.79 0.113 .893 No
difference
5. Company
reputation
4.15 0.67 4.07 0.77 4.18 0.98 0.154 .857 No
difference
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4.2.2 Non-Users Awareness
In case of non-users of LINE MAN (table 4.11), even they have been
known about this application but the barriers that cause them to not use due to easy
finding meals or food from nearby location (Mean = 3.90), followed by they did not
have the problem about ordering food from the specific restaurants (Mean = 3.68), do
not like food delivery because I prefer to eat in the restaurant (Mean = 3.29), and
think delivery fee is too high (Mean = 3.29).
Table 4.11: Barriers of using an on-demand food delivery service
Barriers
(n = 91)
Strongly
disagree
Somewhat
Disagree
Neutral Somewhat
Agree
Strongly
agree X̄ S.D
I did not have the problem
about ordering food from
the specific restaurants
3 5 24 45 14 3.68 0.92
I can easily find meals or
food from nearby
location.
3 5 9 55 19 3.90 0.91
I do not like food delivery
because I prefer to eat in
the restaurant.
6 15 22 43 5 3.29 1.03
I think it is complicate to
download application.
27 33 20 9 2 2.19 1.04
I think it has to do many
steps and fill a lot of
information.
8 23 39 14 7 2.88 1.03
I think delivery fee is too
high.
2 6 53 24 6 3.29 0.78
I am very satisfied with
existing on-demand food
delivery services.
15 17 51 7 1 2.58 0.90
4.2.3 Beliefs in Online Behaviors and Lifestyles
According to table 4.12, the average score shows respondents have the
highest rate about beliefs in online behavior is the pursuit of high quality and
inexpensive (Mean = 4.20), followed by quick and easy (Mean = 4.17), personality
(Mean = 3.55), new products (Mean = 3.49) and the lowest rate by respondents is the
pursuit of consumption (Mean = 3.36).
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Table 4.12: Respondent’s beliefs toward online behavior on degree of the pursuit
on each characteristic
Beliefs
(n = 206)
Strongly
inferior
Inferior Neutral Superior Strongly
superior X̄ S.D
The pursuit of quick and
easy
1 1 38 87 79 4.17 0.78
The pursuit of high quality
and inexpensive
2 36 86 82 4.20 0.76
The pursuit of personality 5 18 75 74 34 3.55 0.95
The pursuit of consumption
experience
7 26 84 63 26 3.36 0.97
The pursuit of new products 3 20 87 66 30 3.49 0.91
In case of lifestyle (Table 4.13), the average score shows that respondents
have the highest agreement toward statement of “I think a mobile phone is the
important thing in my life” (Mean = 4.30), followed by “I do everything online on
mobile” (Mean = 4.01), “I like online service because it is available 24-hour” (Mean
= 3.97), “I like to try new things” (Mean = 3.84), and “I am interested in technology”
(Mean = 3.68). The lowest rate by respondent included “I usually the first person to
know new trends among my friends” (Mean = 3.11) and “I prefer shopping online
rather than going out” (Mean = 2.88).
Table 4.13: Respondent’s agreement toward lifestyle
Lifestyle
(n = 206)
Strongly
disagree
Somewhat
Disagree
Neutral Somewhat
Agree
Strongly
agree X̄ S.D
I like to try new things. 3 12 40 111 40 3.84 0.85
I am usually the first person
to know new trends among
my friends.
8 46 81 58 13 3.11 0.95
I am interested in technology. 4 15 57 96 34 3.68 0.90
I think a mobile phone is the
important thing in my life.
1 6 22 79 98 4.30 0.81
I usually do everything online
on mobile.
1 15 27 101 62 4.01 0.88
I prefer shopping online
rather than going out.
15 65 72 37 17 2.88 1.05
I like online service because
it is available 24-hour.
2 12 37 95 60 3.97 0.89
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Table 4.14 revealed the most used mobile application is social media
(92.2%), followed by entertainment (50.5%), photo and video (37.9%), travel
(29.1%), shopping (22.3%), business or finance (19.9%), and the lowest proportion is
games (18%).
Table 4.14: The most used mobile applications (Top 3)
The most used application (n = 206) Frequency Percentage (%)
Entertainment 104 50.5
Social media (Facebook, Instagram) 190 92.2
Photo and Video 78 37.9
Education (Language Translation) 21 10.2
Finance (Stock) 41 19.9
Business (Bank, News) 41 19.9
Travel 60 29.1
Shopping 46 22.3
Games 37 18.0
4.3 Factor Analysis
In this section, the author conducted exploratory factor analysis to extract
certain purchasing factors with similarity in pattern. Factor analysis will be carried out
with principal axis factoring and rotated by PROMAX method. The result of Kaiser
Meyer Olkin Measure of sampling adequacy (KMO) is 0.814 and Chi-square of
Bartlett’s test is 465.089 with significant level of 0.000. Due to benchmark of KMO
is 0.70 and significant level of Bartlett’s test is lower than 0.05. It explains that the
factor analysis with 14 purchasing items is available.
According to table 4.15, there are six components were extracted from 14
purchasing items. However, only five components have been fulfilled the condition
due to factor loading is above 0.50. The new component is labeled as shown in table
4.16.
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Table 4.15: Pattern Matrix of Factor Loadings
(Promax Rotation)
Purchasing Factors
(n = 92)
Factor
1 2 3 4 5 6
Easy to make order .58
Design of the platform .63
Tracking .40
Security of payment and privacy
Variety of restaurants .79 -.35
Provide information and reviews of menus and
restaurants
.49
Coverage of delivery area .84
Fast and delivery on time .37
Customized services .76 .31
Quality of drivers (taking care of food and manner) .77
Call center or support services .72
Price .73
Promotion/Discount .96
Company Reputation/Reliable .31 .44
*For ease of interpretation, factor loadings smaller than 0.30 are not shown.
Table 4.16: New Labels of factor analysis
New labels Components of Factors
Factor 1: Variety and coverage 1. Variety of restaurants
2. Coverage of delivery area
Factor 2: Price and promotion 1. Price
2. Promotion/Discount
Factor 3: Service 1. Customized services
2. Quality of drivers (taking care of food and manner)
Factor 4: Platform 1. Easy to make order
2. Design of the platform
Factor 5: Support services 1. Call center or support services
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4.4 The Impact of Factors on Implementation of an On-demand Food Delivery
Service
From applying correlation matrix (See Appendix B: Table 1), it shows
correlation coefficient before adjustment and after variables with factor analysis have
ranges between 0.002 – 0.675 and 0.135 – 0.452, which it has lower than 0.80. It
represents no multicollinearity among each independent variable.
In order to identify influencing of motivation factors and key purchasing
factors on adoption of LINE MAN, an on-demand food delivery service, multiple
linear regression was applied. Multiple linear regression allows on analyzing the
effect of many independent variables on dependent variable. For independent
variables, there are five reasons items and 14 items of purchasing factors. Dependent
variable is the frequency of use LINE MAN application. Then, the additional model
that employed extracted five purchasing factors was applied.
In model summary (table 4.17), total 18 items have 0.98% (Adjust R2 =
0.098) predicts frequency of use LINE MAN application. According to F-statistic of
1.550 and significant value is above 0.05, this model is no significantly predicted
frequency of use LINE MAN application at 0.05 significant level. According to beta
coefficients and significant level (one-tailed), it shows the highest influence factors is
tracking system (β = 0.191) and customized service (β = 0.203), which found
significant level is less than 0.05. Additionally, stepwise technique was employed in
order to get the most fitted model for predicting frequency of use LINE MAN
application.
For stepwise model, there are 17 items were removed from the model and
only have one variable left. It has 0.75% of variance on (Adjust R2 = 0.075) predicts
frequency of use LINE MAN application. According to F-statistic of 8.337 and
significant value is less 0.05, this model is a significantly predicted frequency of use
LINE MAN application at 0.05 significant level. According to beta coefficients, it
shows the tracking system (β = 0.215), have a positive impact on frequency of use
LINE MAN application (Sig. < 0.05). Thus, it implies that users who has more
frequent on using LINE MAN application are highly concerns on tracking system
when compare to user with lower usage.
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Table 4.17: The impact of purchasing factors and reasons to use on
implementation of an on-demand food delivery service (n = 92)
Variables
Model 1 (Full) Model 2 (Stepwise)
Beta t Sig. Beta t Sig.
(Constant) 2.832 2.956 .002 2.938 9.262 .000
Easy to make order .093 .912 .182
Design of the platform .067 .743 .230
Tracking system .191 1.758 .041 .215 2.887 .005
Security of payment and privacy -.084 -1.005 .159
Variety of restaurants -.072 -.642 .262
Provide information and reviews of menus and
restaurants
.034 .436 .332
Coverage of delivery area .160 1.264 .105
Delivery on time -.148 -1.277 .103
Customized services .203 1.937 .028
Quality of drivers .074 .642 .262
Call center or support services -.086 -1.027 .154
Price -.128 -1.161 .125
Promotion/Discount .085 .808 .211
Company reputation -.161 -1.551 .063
Save time .018 .155 .439
Save cost .032 .452 .326
Save energy -.080 -1.016 .157
Less Stress .049 .684 .248
Adjusted R Square .098 .075
F 1.550 8.337
Sig. .098 .005
From model summary (table 4.18), total 5 items have 0.67% (Adjust R2 =
0.067) and predict frequency of use LINE MAN application. According to F-statistic
of 2.317 and significant value is above 0.05, this model is no significantly predicted
frequency of use LINE MAN application at 0.05 significant level. According to beta
coefficients, it shows the highest influence factors is service (β = 0.195), followed by
platform (β = 0.172), and support service (β = -0.121) at 0.05 significant level (one-
tailed). It represents both platform and service has positive impacted on
implementation of on-demand food delivery service, while support service has
negative impacted. However, the significant of all factors are all above 0.05. Thus,
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stepwise technique was employed in order to get the most fitted model for predicting
frequency of use LINE MAN application.
According to stepwise model, there are 4 items were removed from the
model and only have one variable left. It has 0.59% of variance on (Adjust R2 =
0.059) and predicts frequency of use LINE MAN application. According to F-statistic
of 5.689 and significant value is less 0.05, this model is a significantly predicted
frequency of use LINE MAN application at 0.05 significant level. According to beta
coefficients, it shows the platform (β = 0.211), have a positive impact on frequency of
use LINE MAN application (Sig. < 0.05). Thus, it implies that users who have more
frequent on using LINE MAN application are highly concerns on platform that easy
to order and provide user friendly in design of interface.
Table 4.18: The impact of extracted purchasing factors on implementation of an
on-demand food delivery service
New Factors
Model 1 (Full) Model 2 (Stepwise)
Beta t Sig. Beta t Sig.
(Constant) 2.886 5.764 .000 2.990 8.296 .000
Variety and coverage .024 .214 .415
Price and promotion -.047 -.555 .290
Service .195 1.847 .034
Platform .172 1.685 .048 .211 2.385 .019
Support services -.121 -1.766 .040
Adjusted R Square .067 .059
F 2.317 5.689
Sig. .050 .019
4.5 Clusters Analysis
In this study, two methods of cluster analysis were employed, hierarchical
cluster analysis and k-mean cluster analysis. For hierarchical cluster analysis, the
author chose Ward’s method and used Squared Euclidean distance as measure with Z-
score standardized method. To classify the number of clusters (k), dendogram and
coefficient from agglomeration schedule was applied. 4.5.1 Defining Clusters
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4.5.1 Defining Clusters
After conducted hierarchical cluster analysis with Ward’s method, it
reveals that dendogram have two major branches. When look at agglomeration
schedule’s coefficients (See Appendix B: Figure 1 and Table 2), it shows the largest
difference gap is found between cluster of 204 and 205, or 2.290.5 and 2,665. Thus,
the appropriate cluster number of this study is equal to two. Then, the k-mean
clustering was conducted in the further part.
From the result of k-mean cluster analysis, it shows two clustered groups.
The first group contains 120 respondents (58.3%) and second cluster include 86
respondents or 41.7%. The result of ANOVA shows a significant difference between
cluster 1 and 2 at 0.05 significant level in all items. According to mean score pattern,
it shows cluster 1 have higher score of beliefs in online behavior, lifestyle, and usage
of an on-demand food delivery service than cluster 2. The highest score of cluster 1 is
on the pursuit of quick and easy (Mean = 4.49), while cluster 2 rated the highest on
the pursuit of high quality and inexpensive (Mean = 3.92). In term of lifestyle, the
highest score of cluster 1 and 2 was found on “I think a mobile phone is the important
thing in my life”, accounted for mean 4.53 and 3.97, respectively.
Table 4.19: Final clustering and ANOVA table
Independent Variables
(n = 206)
Cluster F Sig.
1
(n = 120) 2
(n = 86)
The pursuit of quick and easy 4.49 3.73 62.095 .000
The pursuit of high quality and inexpensive 4.41 3.92 23.250 .000
The pursuit of personality 3.80 3.21 21.298 .000
The pursuit of consumption experience 3.63 3.00 22.927 .000
The pursuit of new products 3.83 3.01 49.600 .000
I like to try new things. 4.17 3.38 52.644 .000
I am usually the first person to know new trends among
my friends.
3.57 2.47 99.316 .000
I am interested in technology. 4.01 3.23 45.112 .000
I think a mobile phone is the important thing in my life. 4.53 3.97 27.784 .000
I usually do everything online on mobile. 4.38 3.50 65.409 .000
I prefer shopping online rather than going out. 3.21 2.43 31.429 .000
I like online service because it is available 24-hour. 4.26 3.56 36.255 .000
Frequency of use (Non1, Non2,L,M,H) 3.03 2.27 24.876 .000
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4.5.2 Consumer Segmentations
After clustered group of respondents, the author derived cross tabulation
analysis and chi-square statistics to identify the pattern of consumer groups. It
consists of mobile application’s usage and demographic data. It defines cluster 1 as a
“Mainstreamers” and cluster 2 as a “Tech traditionalists”.
The test reveals association between cluster memberships and
demographic variables of respondents. It found that a significant level of an on-
demand food delivery service users is less than 0.05, while the rest of all demographic
variables are all above 0.05. It implies that majority of Mainstreamers are medium
users (39.2%), while Tech traditionalists are non-users with awareness of LINE MAN
application (64%), see also Appendix B: Table 3.
According to table 4.20, the test reveals association between cluster
memberships and the most used mobile applications of respondents. It found a
significant level of shopping is less than 0.05, while the rest are all above 0.05. It
implies that Mainstreamers have higher tendency of use shopping application (31.7%)
than Tech traditionalists (9.3%).
Table 4.20: The segmentation of consumers based on mobile applications
Applications Mainstreamers
(n = 120) Tech traditionalists
(n = 86) Total
(n = 206) Chi-
Square
Sig.
n % n % n %
Entertainment 58 48.3% 46 53.5% 104 50.5% .533 .466
Social media
(Facebook, Instagram)
110 91.7% 80 93.0% 190 92.2% .129 .720
Photo and Video 42 35.0% 36 41.9% 78 37.9% 1.002 .317
Education (Language
Translation)
11 9.2% 10 11.6% 21 10.2% .331 .565
Finance (Stock) 20 16.7% 21 24.4% 41 19.9% 1.888 .169
Business (Bank, News) 24 20.0% 17 19.8% 41 19.9% .002 .967
Travel 37 30.8% 23 26.7% 60 29.1% .406 .524
Shopping 38 31.7% 8 9.3% 46 22.3% 14.447 .000
Games 20 16.7% 17 19.8% 37 18.0% .327 .567
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CHAPTER 5
CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusion
The study was designed to learn consumer adoption process of an on-
demand food delivery service through LINE MAN as well as identify influencing of
motivation factors on adoption and barriers to adopt the service. Finally, it was
designed to understand whether there are differences among segments based on their
beliefs, interests, demographic, and psychographic.
According to the respondents’ profile, most of Thai population who is
likely to adopt an on-demand food delivery services – LINE MAN refers to working
women. They are an old millennial, average age 33 years old, well educated, worked
in private sector, and income between 20,000 to 40,000 THB
Figure 5.1: The result of respondents towards adoption model and
decision making process
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From figure 5.1, our findings indicate that the awareness stage of adoption
process is not a problem for LINE MAN. Most of the respondents have awareness
from their friends and its advertisement through LINE messaging application and
social media. However, the main problem is on the interest, evaluation, and trial stage,
it shows on the number of non-users awareness who have known LINE MAN and
other on-demand food delivery services but never try it. The main barrier defines as
the first stage of decision making process – Problem recognition. In the other word,
they have no problem about finding meals because it is easily to find food from
nearby location and there is no specific restaurants in their mind. While a promotion
or discount is the most factor that made them try the service at the first time, friends
are a great impact on the adoption process. It really affects consumer attitude and
purchase decision as well as the acceptance or rejection of the new services.
Obviously, the result shows that technology capability is not the barrier to adopt this
new service. For users’ perspective, information search is one of important stages
because more than 50% of respondents said they sometimes or often do searching
before they use or adopt the service. Interestingly, motivation factors which define as
reasons of applying the on-demand food delivery service (convenient, save time, cost,
energy, and less stress) do not affect for adoption among users. The result reveals that
medium and heavy users are likely similar characteristic in term of occasion and
purchasing factors and it differs from light users. Since, there is some difference on
occasion which are they have no time and they lazy to go to the restaurants. It can
imply that save time is the main reason for applying LINE MAN. Therefore, the more
people are busy or in fast pace environment, the more on-demand food delivery
service is successful.
Essentially, the result shows that the “Mainstreamer” group (Medium
users) is keen on online shopping. Their beliefs in O2O platform are more on the
pursuit of quick and easy, the pursuit of high quality and inexpensive. Besides, their
activities like to try new things and mobile is everything in their life. While the
“Technology traditionalists” (Non-user awareness) group are likely late majority to
adopt new services, they have capability for using technology but might not trust or
paranoid in online environment. Moreover, they are low on the pursuit of personality,
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the pursuit of consumption experience, and the pursuit of new products. This implies
that they are conservative and not trendy person.
In summary, findings of this study suggest that the adoption process in
on-demand delivery service is not straightforward, but attributes of consumer’ s belief
and value towards O2O model, and situational factors in specific environment play an
important role in the process.
5.2 Recommendation
Based on the previous analysis, ease of use and trust in e-commerce
environment (O2O platform) are the main criteria that the on-demand food delivery
service (LINE MAN) has to consider for consumer adoption. Therefore, a seamless
linkage in the operation flow of three main parties (O2O platform, consumers, and
offline businesses) is an important key to adopt this new service.
5.3 Limitation of This Study and Future Research
The research has the limitations which could suggest the way for the
future research. There are two key limitations of this research. First, the limitation of
small number of respondents due to time constraint, therefore, the result is not
strongly enough to define significantly difference among users. Second, this research
focuses only on consumers’ perspective towards LINE MAN application (O2O
platform), but do not study about other parties such as offline restaurants. Moreover, it
lacks the study of consumers’ perspective towards other competitors. Therefore, the
future research can explore other parties in the value chain such as offline restaurants
as well as the competitors such as Food Panda and Uber Eat because the successful of
O2O model is a balance of three main parties which are 1) O2O platform, 2)
Consumer, and 3) Offline businesses. In addition, it should focus on purchasing
criteria of non-users if they use an on demand food delivery service such as ease of
use, security, and unmet needs.
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REFERENCES
Books and Book Articles
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APPENDICES
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APPENDIX A
QUESTIONNAIRE DESIGN
Definition of “On-demand Food Delivery Service” in This Research
Online or on-demand food delivery service only provides a platform for
restaurants and serves meals to customers prepared by partner restaurants which do
not necessarily offer a delivery of their food. Customers had to order meals online
from this platform.
Meals ordered directly online from the restaurants that deliver the order
themselves such KFC, MK, or Pizza Hut are not included.
Section 1: Screening question
1. Do you born between 1981 and 1997?
Yes
No
2. Do you know LINE MAN application?
Yes
No (Skip to question 14)
Section 2: Adoption behaviors and process towards on-demand food delivery
service.
3. How familiar are you with these four LINE MAN services?
Services
Not at all
familiar
1
Slightly
familiar
I’ve heard but
did not know
about it)
2
Somewhat
familiar
I knew it but
never use)
3
Moderately
familiar
I knew it and
used to try)
4
Extremely
familiar
5
Food delivery
Messenger
Groceries
Document and Postal
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4. How did you know LINE MAN application at the first time?
I saw its advertisement or notification in LINE app.
I saw its advertisement in the restaurants.
I saw its advertisement in social media.
I saw LINE MAN drivers on the road.
My friends talked about it or suggested to use.
5. Do you ever use LINE MAN for food delivery service?
Yes
No (Skip to question 13)
6. Which of the following on-demand food delivery services have you used at least
once in the past six month? And how many times have you used on each one?
(Can check more than one)
Food Panda _________ times
LINE MAN _________ times
Uber Eats _________ times
7. What is the reason that makes you try LINE MAN for on-demand food delivery
service at the first time?
I liked a promotion/discount.
I searched for food delivery services and I found LINE MAN.
I want to order food and a friend suggested
I want to order food from the restaurant that have a long queue.
I want to try a new application/new service.
I used to use other LINE MAN services such as messenger.
I was familiar with LINE application and it linked to my LINE account.
8. How many times each month do you use on-demand food delivery services at
average?
__________times per month
9. How much do you agree with following sentences? What occasion do you usually
use on-demand food delivery service?
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Occasions
Strongly
Disagree
Somewhat
Disagree
Neutral Somewhat
Agree
Strongly
Agree
I want to order food from the
specific restaurants where they
are difficult to go
I am do not want to eat outside
or lazy
I am busy and do not have time
to go outside
My friends or family want to
order food delivery.
I do not want to wait a long que
by myself.
10. Why do you use an on-demand food delivery service? Rate each reason below
Reasons Strongly
Inferior
Inferior Neutral Superior Strongly
Superior
Convenient
Save time
Save cost
Save energy
Less Stress
11. When you use on-demand food delivery service, how much search for information
about the service?
Always
Often
Sometimes
Rarely
Never
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Section 3: Purchasing factors and barriers towards on-demand food delivery
service.
12. How much do you rate on degree of important on each factor when choosing an
on-demand food delivery service?
Factors Least
important
1 2 3 4
Most
important
5
1. System quality
1.1 Easy to make order
1.2 Design of the platform
1.3 Tracking system
1.4 Security of payment and privacy
2. Product and service quality
2.1 Variety of restaurants
2.2 Provide information and reviews of
menus and restaurants
2.2 Coverage of delivery area
2.3 Delivery on time
2.4 Customized services
2.5 Quality of drivers (taking care of food
and manner)
2.6 Call center or support services
3. Price
4. Promotion/Discount
5. Company reputation
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13. How much do you agree with following sentences? “What barriers prevent I try
this service because…?
Barriers
Strongly
disagree
Somewhat
Disagree
Neutral Somewhat
Agree
Strongly
Agree
I did not have the problem about
ordering food from the specific
restaurants
I can easily find meals or food
from nearby location.
I do not like food delivery
because I prefer to eat in the
restaurant.
I think it is complicate to
download application.
I think it has to do many steps
and fill a lot of information.
I think delivery fee is too high.
I am very satisfied with existing
on-demand food delivery
services.
Section 4: Characteristics of consumer behavior and motivation factors towards
on demand food delivery service.
14. How much do you rate about your online behavior on degree of the pursuit on
each characteristic?
Characteristics Strongly
Inferior Inferior Neutral Superior
Strongly
superior
The pursuit of quick and easy
The pursuit of high quality and
inexpensive
The pursuit of personality
The pursuit of consumption experience
The pursuit of new products
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Section 5: Consumer’s demographics and psychographics.
15. How much do you agree with following sentences about your lifestyle?
Lifestyle Strongly
Disagree
Somewhat
Disagree Neutral
Somewhat
Agree
Strongly
Agree
I like to try new things.
I am usually the first person to
know new trends among my
friends.
I am interested in technology.
I think a mobile phone is the
important thing in my life.
I usually do everything online on
mobile.
I prefer shopping online rather
than going out.
I like online service because it is
available 24-hour.
16. What kinds of application on your mobile do you use the most? (Please choose
top 3)
Entertainment
Social media
Photo and Video
Education
Finance
Business
Travel
Shopping
Games
17. What is your gender?
Male
Female
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18. What is your age?
20-22
23-26
27-30
31-36
19. What is your current occupation?
Government
Private Sector
Business Owner
Freelance
Others (Studying or Unemployed)
20. What is your highest education level?
High school graduate
Diploma or the equivalent
Bachelor’s degree
Master’s degree
Doctorate degree
21. What is your income?
< 20,0000 THB
20,000 – 40,000 THB
40,001 – 60,000 THB
60,001 – 80,000 THB
> 80,001 THB
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APPENDIX B
SPSS DATA RESULT
Table 1: Frequency distribution of usage rate of LINE MAN’s users
Usage Rate (time/month)
(n=92)
Frequency Percentage (%)
0 11 12.0
1 50 54.3
2 17 18.5
3 6 6.5
4 3 3.3
5 4 4.3
15 1 1.1
Mean 1.62
Median 1.00
SD 1.83
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Table 2: Correlation matrix
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
Easy to make order .475** .423** .361** .370** .339** .488** .432** .294** .250* .216* .229* .254* .368** .053 -.031 .122 -.042 -.108
Design of the platform .332** .288** .285** .227* .231* .336** .400** .238* .151 .096 .114 .253* -.025 -.012 .085 .042 -.075
Tracking system .404** .511** .250* .534** .434** .384** .510** .297** .368** .379** .350** .020 .018 .280** -.063 -.234*
Security of payment and
privacy
.299** .293** .387** .354** .308** .387** .304** .212* .200 .388** .129 .060 .040 .037 -.240*
Variety of restaurants .442** .536** .449** .294** .312** .005 .337** .356** .246* .017 -.119 .140 .218* -.229*
Provide information and
reviews of menus and restaurants
.284** .198 .172 .107 .123 .215* .316** .318** .088 .010 .010 .020 -.122
Coverage of delivery area .522** .299** .312** .258* .319** .222* .298** .048 .076 .171 .012 -.266*
Delivery on time .516** .475** .350** .391** .339** .265* -.071 .109 .166 .087 -.223*
Customized services .531** .156 .217* .157 .333** .009 .155 .118 .017 -.240*
Quality of drivers .451** .311** .233* .434** -.002 .030 .156 .019 -.174
Call center or support services .294** .125 .349** .096 .190 .075 -.136 -.195
Price .684** .279** -.078 .011 .138 -.023 -.033
Promotion/Discount .319** .019 -.063 .161 .047 -.155
Company reputation .015 -.019 .239* -.085 -.148
Convenient .467** -.448** -.366** -.675**
Save time -.327** -.352** -.630**
Save cost -.319** .055
Save energy .124
Less Stress
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
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Table 3: Correlation matrix (adjusted)
1 2 3 4 5
Variety and coverage .387** .396** .452** .135
Price and promotion .284** .219* .226*
Service .395** .343**
Platform .213*
Support services
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Figure 1: Dendogram
Table 2: Results of hierarchical cluster analysis
Agglomeration Schedule Coefficients Coefficient Difference
196 1594.4 50.5
197 1644.9 51.6
198 1696.5 60.1
199 1756.6 75.7
200 1832.3 85.1
201 1917.4 106.8
202 2024.2 124.2
203 2148.5 142.1
204 2290.5 374.5
205 2665.0
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Table 5: The segmentation of consumers based on demographics
Demographics Mainstreamers
(n = 120)
Tech
Traditionalist
(n = 86)
Total
(n = 206)
Chi-
Square
Sig.
n % n % n %
Male 32 26.7% 26 30.2% 58 28.2% .315 .575
Female 88 73.3% 60 69.8% 148 71.8%
20-22 years 0 0.0% 3 3.5% 3 1.5% 4.477 .214
23-26 years 20 16.7% 16 18.6% 36 17.5%
27-30 years 37 30.8% 25 29.1% 62 30.1%
31-36 years 63 52.5% 42 48.8% 105 51.0%
Government 12 10.0% 8 9.3% 20 9.7% 3.500 .478
Private Sector 79 65.8% 54 62.8% 133 64.6%
Business Owner 11 9.2% 11 12.8% 22 10.7%
Freelance 11 9.2% 4 4.7% 15 7.3%
Others (Studying or
Unemployed)
7 5.8% 9 10.5% 16 7.8%
High school graduate 1 .8% 0 0.0% 1 .5% 5.595 .232
Diploma or the
equivalent
2 1.7% 0 0.0% 2 1.0%
Bachelor’s degree 61 50.8% 54 62.8% 115 55.8%
Master’s degree 54 45.0% 32 37.2% 86 41.7%
Doctorate degree 2 1.7% 0 0.0% 2 1.0%
< 20,000 THB 10 8.3% 13 15.1% 23 11.2% 3.909 .563
20,000-40,000 THB 53 44.2% 33 38.4% 86 41.7%
40,001-60,000 THB 19 15.8% 9 10.5% 28 13.6%
40,001-60,000 THB 16 13.3% 14 16.3% 30 14.6%
60,001-80,000 THB 7 5.8% 5 5.8% 12 5.8%
> 80,001 THB 15 12.5% 12 14.0% 27 13.1%
Non User-No
Awareness
14 11.7% 9 10.5% 23 11.2% 35.426 .000
Non User- Awareness 36 30.0% 55 64.0% 91 44.2%
Light Users 13 10.8% 13 15.1% 26 12.6%
Medium Users 47 39.2% 8 9.3% 55 26.7%
Heavy Users 10 8.3% 1 1.2% 11 5.3%
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BIOGRAPHY
Name Miss. Noppanut Talabpetch
Date of Birth July 3, 1984
Educational Attainment
2007: Bachelor of Science in Material Science
(Gems and Jewelry), Faculty of Science
Srinakharinwirot University
Work Experiences Project Manager
Gems Pavilion Creation Co., Ltd
Publication 2018
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